Text summarization is the process of constructing a concise, cohesive, and fluent summary of a lengthy text document [1]}. It gives us a brief context of the story. Statutes (established laws) and Precedents (prior cases) are the two primary sources of law for countries that follow the Common Law System like India [2]}. Hence, there are hundreds of prior cases that lawyers must go through. Legal documents can be lengthy. The abbreviations and terminology used in Legal documents are different from the standard language. Manual drafting of case summaries is a process that takes a lot of time. Automatic summarization of texts is possible because of the advancement of Artificial Intelligence (AI) and Machine Learning (ML). Thousands of hours of man-labour can be reduced with the help of State-of-the-Art Machine Learning models. It is also useful for beginners and ordinary citizens to understand a judgement. More than 4.70 crore cases are pending in various courts in India [3]}. The use of automatic summarization of texts will also significantly reduce the number. Several legal text summarization techniques and tools have been reported in the past based on UK [4]}, Canadian [5]} and Australian [6]} court judgements. As each country has its structure and abbreviations in their Legal documents it is not suitable to use those tools and techniques for other countries. To train a model for domain-specific text summarization, we need a lot of data. As there is no publicly available dataset for summarization of Indian legal documents present, so we have proposed a different methodology to summarize them. Text summarization techniques are classified into two categories. An abstractive summarization recognises the language in the text and adds novel words to the summary if necessary [7]}, [8]} and in extractive summarization, a summary is formed from a subset of sentences [9]}. In this paper, The Indian legal texts have been normalised as a general text based on our proposed methodology. Next, two domain-independent models have been used for summarization. The authors have tried to conduct extractive summarization using BART [10]} and abstractive summarization with PEGASUS [11]}.
Section II presents the related work behind the proposed methodology. Section III discusses a detailed explanation of the proposed methodology. Section IV presents the evaluation of the work compared to the traditional one. Finally, Section V concludes the paper along with its future applications.
